Semi-supervised Learning for Automatic Prosodic Event Detection Using Co-training Algorithm

Proceedings of the 47th Annual Meeting of the ACL and the 4th IJCNLP of the AFNLP, pages 540–548, Suntec, Singapore, 2-7 August 2009. c⃝2009 ACL and AFNLP Semi-supervised Learning for Automatic Prosodic Event Detection Using Co-training Algorithm Je Hun Jeon and Yang Liu Computer Science Department The University of Texas at Dallas, Richardson, TX, USA {jhjeon,yangl}@hlt.utdallas.edu Abstract Most of previous approaches to automatic prosodic event detection are based on su- pervised learning, relying on the avail- ability of a corpus that is annotated with the prosodic labels of interest in order to train the classification models. However, creating such resources is an expensive and time-consuming task. In this paper, we exploit semi-supervised learning with the co-training algorithm for automatic de- tection of coarse level representation of prosodic events such as pitch accents, in- tonational phrase boundaries, and break indices. We propose a confidence-based method to assign labels to unlabeled data and demonstrate improved results using this method compared to the widely used agreement-based method. In addition, we examine various informative sample selec- tion methods. In our experiments on the Boston University radio news corpus, us- ing only a small amount of the labeled data as the initial training set, our proposed la- beling method combined with most confi- dence sample selection can effectively use unlabeled data to improve performance and finally reach performance closer to that of the supervised method using all the training data. 1 Introduction Prosody represents suprasegmental information in speech since it normally extends over more than one phoneme segment. Prosodic phenomena man- ifest themselves in speech in different ways, in- cluding changes in relative intensity to emphasize specific words or syllables, variations of the fun- damental frequency range and contour, and subtle timing variations, such as syllable lengthening and insertion of pause. In spoken utterances, speakers use prosody to convey emphasis, intent, attitude, and emotion. These are important cues to aid the listener for interpretation of speech. Prosody also plays an important role in automatic spoken lan- guage processing tasks, such as speech act detec- tion and natural speech synthesis, because it in- cludes aspect of higher level information that is not completely revealed by segmental acoustics or lexical information. To represent prosodic events for the categorical annotation schemes, one of the most popular label- ing schemes is the Tones and Break Indices (ToBI) framework (Silverman et al., 1992). The most im- portant prosodic phenomena captured within this framework include pitch accents (or prominence) and prosodic phrase boundaries. Within the ToBI framework, prosodic phrasing refers to the per- ceived grouping of words in an utterance, and accent refers to the greater perceived strength or emphasis of some syllables in a phrase. Cor- pora annotated with prosody information can be used for speech analysis and to learn the relation- ship between prosodic events and lexical, syntac- tic and semantic structure of the utterance. How- ever, it is very expensive and time-consuming to perform prosody labeling manually. Therefore, automatic labeling of prosodic events is an attrac- tive alternative …

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